To the best of authors' knowledge the best reported misclassification rate is \(15.2\%\)  [1]}, which is achieved by intricately designed convolutional neural network. The average misclassification rate of \(11.9\%\)  we achieved with our vectorized model, using \(v_Q\)  featurization plus the above 4 physical features, with Random Forest, outperforms this state-of-the-art result. In addition, notice that our method is very efficient and easy to apply as we use simple featurization and simple out-of-the-box machine learning algorithms. Moreover, we only need to tune a single benign hyperparameter in Random Forest for example, namely, the number of estimators, while in CNN one needs to tune many hyperparameters in addition to the design of a useful architecture.
